41 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions at Aarhus University
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of Prof. Georg Madsen, with regular shorter research stays at Aarhus University. The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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The Section for Electrical Energy Technology at the Department of Electrical and Computer Engineering (ECE), Aarhus University, is in a phase of rapid growth in both education and research
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Are you analytically sharp and interested in how artificial intelligence can support learning and teaching? Then we have an exciting opportunity for you at Aarhus BSS. We are looking for a student
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others. Essential: Strong data analysis and machine learning skills and experience with PyTorch (or equivalent frameworks). Hands-on experience with data representation and embeddings, ideally applied
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: Overcoming Inequity in Embodied Learning in Danish Vocational Education and Beyond, funded by Independent Research Fund Denmark. This is a full-time (37 hours per week), fixed-term (24 months) postdoctoral
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consider task success, generalisation, reliability and computational efficiency. The goal is original research for leading machine-learning, computer-vision and robotics venues. The successful candidate will
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designing and building visualization dashboards. Research experience in human-centered AI, or in the integration of AI and machine learning methods into interactive visualization and analysis systems. Strong
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uncertainty, learn, and coordinate - and how these processes compare with AI. You are likely studying cognitive science, psychology, behavioral science, human-computer interaction, or another field with a
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for Statistical and computational Methods for Advanced Research to Transform biomedicine (SMARTbiomed) within the field of statistical and machine learning methods development for genetic analysis and causal